Publications by Francesca Miccolis

A gene fusion is a chromosomal aberration from juxtaposing separate genes. Since some gene fusions are involved in tumorigenesis, proper gene fusion investigation and analysis are crucial in the literature. After DNA/RNA sample extraction, detecting gene fusions requires first gene fusion detection tools, which usually provide many false positives. Given the high experimental costs in wet lab validation of a single fusion, gene fusion prioritization tools were made available over the years to significantly narrow down candidate gene fusions for validation (e.g., Oncofuse, Pegasus, DEEPrior, ChimerDriver). Although a few reviews about gene fusion detection tools are available, a benchmark on prioritization tools is not available yet in the literature. The aim of this paper is twofold: 1. to provide a curated dataset for a fair gene fusion prioritization tool evaluation. 2. to develop a proper comparison based on time, resources, and tool confidence on selected gene fusions. Based on this benchmark, it can be stated that ChimerDriver is the most reliable tool for prioritizing oncogenic fusions.

Whole Slide Images (WSIs) are crucial in histological diagnostics, providing high-resolution insights into cellular structures. In addition to challenges like the gigapixel scale of WSIs and the lack of pixel-level annotations, privacy restrictions further complicate their analysis. For instance, in a hospital network, different facilities need to collaborate on WSI analysis without the possibility of sharing sensitive patient data. A more practical and secure approach involves sharing models capable of continual adaptation to new data. However, without proper measures, catastrophic forgetting can occur. Traditional continual learning techniques rely on storing previous data, which violates privacy restrictions. To address this issue, this paper introduces Context Optimization Multiple Instance Learning (CooMIL), a rehearsal-free continual learning framework explicitly designed for WSI analysis. It employs a WSI-specific prompt learning procedure to adapt classification models across tasks, efficiently preventing catastrophic forgetting. Evaluated on four public WSI datasets from TCGA projects, our model significantly outperforms state-of-the-art methods within the WSI-based continual learning framework. The source code is available at https://github.com/FrancescaMiccolis/CooMIL.